Soybean is one of the major allergenic foods in the United States, European Union, and many other countries. Soybean is commonly processed into different types of soy ingredients to achieve desired properties. The processing, however, may affect the protein profiles and protein structure, thus affecting the detection of soy proteins. Mass spectrometry is a potential alternative to the traditional immunoassays for the detection of soy-derived ingredients in foods. This study aims to develop an LC-MS/MS method that uniformly detects different types of soy-derived ingredients. Target peptides applicable to the detection of six commercial soy ingredients were identified based on the results of MS label-free quantification and a set of selection criteria. The results indicated that soy ingredient processing can result in different protein profiles, mostly affecting the content of minor seed storage proteins. Six soy ingredients were then individually incurred into cookie matrices at different levels. Sample preparation methods were optimized, and a distinct improvement in peptide performance was observed after optimization. Cookies and dough incurred with different soy ingredients at 100 ppm total soy protein showed a similar level of peptide recovery, demonstrating the advantage of the MS method in detecting processed soy ingredients in comparison with antibody-based methods.